Data Architecture#2737
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5 Days·Classroom
Enterprise Data Architecture & Data Modeling Training Course
Shows how to turn business requirements into conceptual, logical and physical data models and an enterprise data architecture that supports integration.
DARC · Data ArchitectureAll Levels#2736ClassroomEnglish , Arabic
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Free substitution at any time · 30+ days' notice: full refund minus 10%. Cancellation policy
Sectors:Data, Analytics & Digital TransformationCategories:Data Architecture
Course Overview
This course develops practical skills in designing enterprise data architectures and data models that support data integration, governance, analytics, and reliable information management across an organization. Participants learn how to translate business requirements into structured data architectures, define relationships between data entities, and establish consistent models that support operational systems, reporting platforms, and enterprise-wide decision-making. The programme covers conceptual, logical, and physical data modeling, enterprise data architecture principles, relational and dimensional modeling, data integration, metadata management, data quality, and governance requirements. Through practical exercises, participants learn to document data flows, define business entities and relationships, establish modeling standards, and design scalable data structures aligned with organizational objectives and technology environments.
Learning Objectives
- By the end of the course, participants will be able to:
- Explain enterprise data architecture principles and their role in organizational information management.
- Translate business requirements into conceptual, logical, and physical data models.
- Identify core business entities, attributes, relationships, and data ownership responsibilities.
- Apply relational and dimensional modeling techniques to different business requirements.
- Evaluate data architecture patterns for operational systems, analytical platforms, and integrated environments.
- Define data standards, naming conventions, and modeling documentation requirements.
- Analyze data integration requirements across applications, databases, and enterprise platforms.
- Incorporate data quality, metadata, security, and governance considerations into architecture design.
- Assess data models for consistency, scalability, maintainability, and performance.
- Develop an enterprise data architecture proposal aligned with business priorities and technology constraints.
Who Should Attend
This course is designed for enterprise architects, data architects, data modelers, database administrators, data engineers, solution architects, and information management professionals responsible for designing or maintaining organizational data environments.
It is also suitable for IT managers, data governance specialists, business intelligence professionals, systems analysts, and digital transformation teams involved in enterprise data integration and modernization. Professionals from government entities, ministries, banks, financial institutions, oil and gas companies, industrial organizations, and large corporations will benefit from learning how to structure enterprise data consistently and align data architecture decisions with business requirements. Familiarity with databases and basic data management concepts is recommended.
Learning Outcomes
- Describe the main components and principles of an enterprise data architecture.
- Convert business requirements into documented data architecture specifications.
- Develop conceptual, logical, and physical data models for defined business domains.
- Identify entities, attributes, primary keys, foreign keys, and business relationships.
- Select suitable relational and dimensional modeling approaches for different workloads.
- Document data flows and dependencies between source systems and target platforms.
- Establish consistent data definitions, naming standards, and modeling conventions.
- Identify data quality, metadata, security, and governance requirements during design.
- Evaluate models for redundancy, integrity, scalability, and operational suitability.
- Present a structured data architecture and modeling proposal supported by business requirements.
Course Outline
Enterprise Data Architecture Principles and Business Alignment
- Understanding enterprise data architecture and its role in organizational strategy.
- Distinguishing data architecture from application architecture, infrastructure architecture, and database design.
- Examining the relationship between business capabilities, information requirements, and data domains.
- Identifying major data architecture components, including source systems, integration layers, storage platforms, and analytical environments.
- Understanding centralized, decentralized, and federated data architecture approaches.
- Mapping business information requirements to data architecture components.
- Identifying data ownership, stewardship, and accountability responsibilities.
- Reviewing architecture principles, standards, constraints, and design decisions.
- Practical Exercise: Develop a high-level enterprise data architecture diagram based on an organization's business functions, data sources, and reporting requirements.
Conceptual, Logical, and Physical Data Modeling
- Understanding the purpose and scope of conceptual, logical, and physical data models.
- Identifying business entities, attributes, identifiers, and relationships.
- Developing entity-relationship diagrams and defining cardinality and participation constraints.
- Applying normalization principles to reduce redundancy and improve data integrity.
- Defining primary keys, foreign keys, candidate keys, and referential constraints.
- Mapping business terminology to standardized data definitions.
- Converting logical models into physical database structures.
- Evaluating data types, constraints, indexing considerations, and database-specific requirements.
- Practical Exercise: Develop conceptual, logical, and physical data models for a selected business process and document the key design decisions.
Enterprise Data Modeling, Integration, and Analytical Structures
- Establishing shared data definitions across business domains.
- Identifying master data, reference data, transactional data, and analytical data requirements.
- Designing relationships between customer, supplier, product, financial, operational, and organizational data.
- Applying relational modeling techniques to transactional systems.
- Understanding dimensional modeling concepts, including fact tables, dimension tables, and grain.
- Evaluating data integration patterns for multiple source systems.
- Mapping source-to-target data structures and documenting transformation requirements.
- Managing inconsistent identifiers, duplicate entities, and conflicting data definitions across applications.
- Practical Exercise: Design an integrated data model that connects multiple business domains and supports both operational processes and management reporting.
Data Governance, Metadata, Quality, and Architecture Standards
- Integrating data governance requirements into enterprise architecture and model design.
- Defining data ownership, stewardship, classification, and access responsibilities.
- Establishing business glossaries, data dictionaries, and metadata documentation.
- Applying naming conventions, modeling standards, and reusable design patterns.
- Identifying data quality dimensions, including accuracy, completeness, consistency, and validity.
- Incorporating security, privacy, retention, and regulatory requirements into data structures.
- Establishing data lineage and documenting movement between source and target systems.
- Assessing model consistency and managing changes to shared data definitions.
- Practical Exercise: Develop a data governance and modeling standards checklist for an enterprise data domain, including ownership, metadata, quality controls, and access requirements.
Architecture Evaluation, Optimization, and Integrated Design Workshop
- Evaluating enterprise data architecture against business objectives and operational requirements.
- Reviewing models for redundancy, referential integrity, maintainability, and scalability.
- Assessing design trade-offs between normalization, denormalization, and analytical performance.
- Identifying integration dependencies and potential data architecture bottlenecks.
- Evaluating data storage and modeling choices for operational and analytical workloads.
- Documenting architecture decisions, assumptions, dependencies, and constraints.
- Establishing model review procedures and change management controls.
- Developing a phased improvement roadmap for enterprise data architecture.
- Final Workshop: Prepare and present an enterprise data architecture and modeling proposal that includes business requirements, architecture diagrams, conceptual and logical models, integration flows, governance standards, data quality controls, and recommendations for implementation.
Upcoming Dates
Selected date
Muscat · 25 – 29 Jan 2027 · Classroom
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| City | Dates | Price per Participant | Early Bird | |
|---|---|---|---|---|
HamburgScheduled | 2 – 6 Nov 2026 | €5,800 | — | |
IstanbulScheduled | 9 – 13 Nov 2026 | €4,700 | — | |
JeddahScheduled | 15 – 19 Nov 2026 | €4,200 | — | |
CairoScheduled | 22 – 26 Nov 2026 | €3,300 | €2,970until 2 Nov | |
KigaliScheduled | 23 – 27 Nov 2026 | €5,100 | €4,590until 2 Nov | |
Kuala LumpurScheduled | 30 Nov – 4 Dec 2026 | €4,400 | €3,960until 9 Nov | |
LisbonScheduled | 7 – 11 Dec 2026 | €5,400 | €4,860until 16 Nov | |
LondonScheduled | 14 – 18 Dec 2026 | €5,600 | €5,040until 23 Nov | |
Cape TownScheduled | 14 – 18 Dec 2026 | €5,100 | €4,590until 23 Nov | |
MadinahScheduled | 14 – 18 Dec 2026 | €4,200 | €3,780until 23 Nov | |
MadridScheduled | 21 – 25 Dec 2026 | €5,400 | €4,860until 30 Nov | |
ManamaScheduled | 28 Dec 2026 – 1 Jan 2027 | €4,700 | €4,230until 7 Dec | |
DammamScheduled | 3 – 7 Jan 2027 | €4,200 | €3,780until 14 Dec | |
MarbellaScheduled | 4 – 8 Jan 2027 | €5,400 | €4,860until 14 Dec | |
MarrakeshScheduled | 11 – 15 Jan 2027 | €4,300 | €3,870until 21 Dec | |
DohaScheduled | 17 – 21 Jan 2027 | €4,400 | €3,960until 28 Dec | |
MilanScheduled | 18 – 22 Jan 2027 | €6,000 | €5,400until 28 Dec | |
MuscatScheduled | 25 – 29 Jan 2027 | €5,300 | €4,770until 4 Jan | |
OnlineScheduled | 31 Jan – 4 Feb 2027 | €1,700 | €1,530until 11 Jan | |
ParisScheduled | 1 – 5 Feb 2027 | €5,700 | €5,130until 11 Jan | |
DubaiScheduled | 7 – 11 Feb 2027 | €4,400 | €3,960until 18 Jan | |
RiyadhScheduled | 8 – 12 Feb 2027 | €4,200 | €3,780until 18 Jan | |
RomeScheduled | 15 – 19 Feb 2027 | €5,800 | €5,220until 25 Jan | |
SalalahScheduled | 22 – 26 Feb 2027 | €5,300 | €4,770until 1 Feb | |
ErbilScheduled | 28 Feb – 4 Mar 2027 | €3,100 | €2,790until 8 Feb | |
Sharm El-SheikhScheduled | 1 – 5 Mar 2027 | €4,300 | €3,870until 8 Feb | |
ViennaScheduled | 8 – 12 Mar 2027 | €5,700 | €5,130until 15 Feb | |
AbujaScheduled | 15 – 19 Mar 2027 | €5,100 | €4,590until 22 Feb | |
AccraScheduled | 15 – 19 Mar 2027 | €5,100 | €4,590until 22 Feb | |
MakkahScheduled | 22 – 26 Mar 2027 | €4,200 | €3,780until 1 Mar |
Available in: Istanbul · Dubai · London · Riyadh · Doha · Amsterdam
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